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Development, Implementation, and Evaluation of an In-Hospital Optimized Early Warning Score for Patient Deterioration

Background. Identification of patients at risk of deteriorating during their hospitalization is an important concern. However, many off-shelf scores have poor in-center performance. In this article, we report our experience developing, implementing, and evaluating an in-hospital score for deteriorat...

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Autores principales: O’Brien, Cara, Goldstein, Benjamin A., Shen, Yueqi, Phelan, Matthew, Lambert, Curtis, Bedoya, Armando D., Steorts, Rebecca C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6956604/
https://www.ncbi.nlm.nih.gov/pubmed/31976373
http://dx.doi.org/10.1177/2381468319899663
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author O’Brien, Cara
Goldstein, Benjamin A.
Shen, Yueqi
Phelan, Matthew
Lambert, Curtis
Bedoya, Armando D.
Steorts, Rebecca C.
author_facet O’Brien, Cara
Goldstein, Benjamin A.
Shen, Yueqi
Phelan, Matthew
Lambert, Curtis
Bedoya, Armando D.
Steorts, Rebecca C.
author_sort O’Brien, Cara
collection PubMed
description Background. Identification of patients at risk of deteriorating during their hospitalization is an important concern. However, many off-shelf scores have poor in-center performance. In this article, we report our experience developing, implementing, and evaluating an in-hospital score for deterioration. Methods. We abstracted 3 years of data (2014–2016) and identified patients on medical wards that died or were transferred to the intensive care unit. We developed a time-varying risk model and then implemented the model over a 10-week period to assess prospective predictive performance. We compared performance to our currently used tool, National Early Warning Score. In order to aid clinical decision making, we transformed the quantitative score into a three-level clinical decision support tool. Results. The developed risk score had an average area under the curve of 0.814 (95% confidence interval = 0.79–0.83) versus 0.740 (95% confidence interval = 0.72–0.76) for the National Early Warning Score. We found the proposed score was able to respond to acute clinical changes in patients’ clinical status. Upon implementing the score, we were able to achieve the desired positive predictive value but needed to retune the thresholds to get the desired sensitivity. Discussion. This work illustrates the potential for academic medical centers to build, refine, and implement risk models that are targeted to their patient population and work flow.
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spelling pubmed-69566042020-01-23 Development, Implementation, and Evaluation of an In-Hospital Optimized Early Warning Score for Patient Deterioration O’Brien, Cara Goldstein, Benjamin A. Shen, Yueqi Phelan, Matthew Lambert, Curtis Bedoya, Armando D. Steorts, Rebecca C. MDM Policy Pract Article Background. Identification of patients at risk of deteriorating during their hospitalization is an important concern. However, many off-shelf scores have poor in-center performance. In this article, we report our experience developing, implementing, and evaluating an in-hospital score for deterioration. Methods. We abstracted 3 years of data (2014–2016) and identified patients on medical wards that died or were transferred to the intensive care unit. We developed a time-varying risk model and then implemented the model over a 10-week period to assess prospective predictive performance. We compared performance to our currently used tool, National Early Warning Score. In order to aid clinical decision making, we transformed the quantitative score into a three-level clinical decision support tool. Results. The developed risk score had an average area under the curve of 0.814 (95% confidence interval = 0.79–0.83) versus 0.740 (95% confidence interval = 0.72–0.76) for the National Early Warning Score. We found the proposed score was able to respond to acute clinical changes in patients’ clinical status. Upon implementing the score, we were able to achieve the desired positive predictive value but needed to retune the thresholds to get the desired sensitivity. Discussion. This work illustrates the potential for academic medical centers to build, refine, and implement risk models that are targeted to their patient population and work flow. SAGE Publications 2020-01-10 /pmc/articles/PMC6956604/ /pubmed/31976373 http://dx.doi.org/10.1177/2381468319899663 Text en © The Author(s) 2020 https://creativecommons.org/licenses/by-nc/4.0/ This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Article
O’Brien, Cara
Goldstein, Benjamin A.
Shen, Yueqi
Phelan, Matthew
Lambert, Curtis
Bedoya, Armando D.
Steorts, Rebecca C.
Development, Implementation, and Evaluation of an In-Hospital Optimized Early Warning Score for Patient Deterioration
title Development, Implementation, and Evaluation of an In-Hospital Optimized Early Warning Score for Patient Deterioration
title_full Development, Implementation, and Evaluation of an In-Hospital Optimized Early Warning Score for Patient Deterioration
title_fullStr Development, Implementation, and Evaluation of an In-Hospital Optimized Early Warning Score for Patient Deterioration
title_full_unstemmed Development, Implementation, and Evaluation of an In-Hospital Optimized Early Warning Score for Patient Deterioration
title_short Development, Implementation, and Evaluation of an In-Hospital Optimized Early Warning Score for Patient Deterioration
title_sort development, implementation, and evaluation of an in-hospital optimized early warning score for patient deterioration
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6956604/
https://www.ncbi.nlm.nih.gov/pubmed/31976373
http://dx.doi.org/10.1177/2381468319899663
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